Overview

Dataset statistics

Number of variables16
Number of observations791
Missing cells32
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory137.3 KiB
Average record size in memory177.8 B

Variable types

DateTime2
Numeric9
Categorical2
Text3

Alerts

date has constant value ""Constant
recovered has constant value ""Constant
recovered_diff has constant value ""Constant
confirmed is highly overall correlated with deaths and 1 other fieldsHigh correlation
deaths is highly overall correlated with confirmed and 1 other fieldsHigh correlation
confirmed_diff is highly overall correlated with deaths_diff and 1 other fieldsHigh correlation
deaths_diff is highly overall correlated with confirmed_diff and 1 other fieldsHigh correlation
active is highly overall correlated with confirmed and 1 other fieldsHigh correlation
active_diff is highly overall correlated with confirmed_diff and 1 other fieldsHigh correlation
lat has 16 (2.0%) missing valuesMissing
long has 16 (2.0%) missing valuesMissing
confirmed has 11 (1.4%) zerosZeros
deaths has 33 (4.2%) zerosZeros
confirmed_diff has 441 (55.8%) zerosZeros
deaths_diff has 630 (79.6%) zerosZeros
active has 8 (1.0%) zerosZeros
active_diff has 436 (55.1%) zerosZeros
fatality_rate has 36 (4.6%) zerosZeros

Reproduction

Analysis started2023-08-04 07:29:00.305107
Analysis finished2023-08-04 07:30:47.052658
Duration1 minute and 46.75 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

date
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
Minimum2023-03-09 00:00:00
Maximum2023-03-09 00:00:00
2023-08-04T15:30:47.116285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:47.223847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

confirmed
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct780
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean855303.15
Minimum0
Maximum38618509
Zeros11
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:47.364146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3464.5
Q161795.5
median211158
Q3673208
95-th percentile3421827.5
Maximum38618509
Range38618509
Interquartile range (IQR)611412.5

Descriptive statistics

Standard deviation2397051.2
Coefficient of variation (CV)2.802575
Kurtosis117.06182
Mean855303.15
Median Absolute Deviation (MAD)191478
Skewness9.2535468
Sum6.7654479 × 108
Variance5.7458546 × 1012
MonotonicityNot monotonic
2023-08-04T15:30:47.543042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.4%
13 2
 
0.3%
209451 1
 
0.1%
103374 1
 
0.1%
356814 1
 
0.1%
168582 1
 
0.1%
188152 1
 
0.1%
155600 1
 
0.1%
47057 1
 
0.1%
404282 1
 
0.1%
Other values (770) 770
97.3%
ValueCountFrequency (%)
0 11
1.4%
4 1
 
0.1%
9 1
 
0.1%
13 2
 
0.3%
29 1
 
0.1%
42 1
 
0.1%
49 1
 
0.1%
103 1
 
0.1%
173 1
 
0.1%
712 1
 
0.1%
ValueCountFrequency (%)
38618509 1
0.1%
30615522 1
0.1%
20656177 1
0.1%
17042722 1
0.1%
12129699 1
0.1%
11526994 1
0.1%
10044957 1
0.1%
9970937 1
0.1%
8466220 1
0.1%
8138129 1
0.1%

deaths
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct706
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8700.0468
Minimum0
Maximum186138
Zeros33
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:47.713352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q1534.5
median2548
Q38297
95-th percentile36014.5
Maximum186138
Range186138
Interquartile range (IQR)7762.5

Descriptive statistics

Standard deviation19993.092
Coefficient of variation (CV)2.2980441
Kurtosis33.23648
Mean8700.0468
Median Absolute Deviation (MAD)2410
Skewness5.2307259
Sum6881737
Variance3.9972371 × 108
MonotonicityNot monotonic
2023-08-04T15:30:47.878231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
4.2%
2 11
 
1.4%
1 5
 
0.6%
7 4
 
0.5%
4 3
 
0.4%
834 3
 
0.4%
3 3
 
0.4%
1031 2
 
0.3%
225 2
 
0.3%
38 2
 
0.3%
Other values (696) 723
91.4%
ValueCountFrequency (%)
0 33
4.2%
1 5
 
0.6%
2 11
 
1.4%
3 3
 
0.4%
4 3
 
0.4%
5 2
 
0.3%
6 2
 
0.3%
7 4
 
0.5%
8 2
 
0.3%
9 1
 
0.1%
ValueCountFrequency (%)
186138 1
0.1%
179039 1
0.1%
161512 1
0.1%
160941 1
0.1%
148424 1
0.1%
144933 1
0.1%
130472 1
0.1%
119010 1
0.1%
102595 1
0.1%
101492 1
0.1%

recovered
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
0
791 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters791
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 791
100.0%

Length

2023-08-04T15:30:48.026983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T15:30:48.133168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 791
100.0%

Most occurring characters

ValueCountFrequency (%)
0 791
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 791
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 791
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 791
100.0%

confirmed_diff
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct227
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.38685
Minimum0
Maximum26285
Zeros441
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:48.263323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q389
95-th percentile739.5
Maximum26285
Range26285
Interquartile range (IQR)89

Descriptive statistics

Standard deviation1412.1194
Coefficient of variation (CV)5.7546662
Kurtosis197.62264
Mean245.38685
Median Absolute Deviation (MAD)0
Skewness12.760423
Sum194101
Variance1994081.2
MonotonicityNot monotonic
2023-08-04T15:30:48.431263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441
55.8%
1 12
 
1.5%
14 8
 
1.0%
3 7
 
0.9%
11 6
 
0.8%
4 6
 
0.8%
2 5
 
0.6%
90 5
 
0.6%
9 5
 
0.6%
8 5
 
0.6%
Other values (217) 291
36.8%
ValueCountFrequency (%)
0 441
55.8%
1 12
 
1.5%
2 5
 
0.6%
3 7
 
0.9%
4 6
 
0.8%
5 2
 
0.3%
6 3
 
0.4%
7 4
 
0.5%
8 5
 
0.6%
9 5
 
0.6%
ValueCountFrequency (%)
26285 1
0.1%
19402 1
0.1%
10335 1
0.1%
10320 1
0.1%
8332 1
0.1%
7863 1
0.1%
6308 1
0.1%
5283 1
0.1%
3602 1
0.1%
3551 1
0.1%

deaths_diff
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3438685
Minimum-1
Maximum431
Zeros630
Zeros (%)79.6%
Negative1
Negative (%)0.1%
Memory size44.6 KiB
2023-08-04T15:30:48.575028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median0
Q30
95-th percentile8
Maximum431
Range432
Interquartile range (IQR)0

Descriptive statistics

Standard deviation18.059342
Coefficient of variation (CV)7.7049296
Kurtosis422.99695
Mean2.3438685
Median Absolute Deviation (MAD)0
Skewness19.05543
Sum1854
Variance326.13983
MonotonicityNot monotonic
2023-08-04T15:30:48.715268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 630
79.6%
1 49
 
6.2%
2 22
 
2.8%
3 17
 
2.1%
4 16
 
2.0%
5 10
 
1.3%
11 6
 
0.8%
9 4
 
0.5%
7 3
 
0.4%
8 3
 
0.4%
Other values (24) 31
 
3.9%
ValueCountFrequency (%)
-1 1
 
0.1%
0 630
79.6%
1 49
 
6.2%
2 22
 
2.8%
3 17
 
2.1%
4 16
 
2.0%
5 10
 
1.3%
6 2
 
0.3%
7 3
 
0.4%
8 3
 
0.4%
ValueCountFrequency (%)
431 1
0.1%
205 1
0.1%
84 1
0.1%
53 1
0.1%
50 1
0.1%
48 1
0.1%
47 1
0.1%
43 1
0.1%
42 1
0.1%
39 1
0.1%

recovered_diff
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
0
791 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters791
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 791
100.0%

Length

2023-08-04T15:30:48.851715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-04T15:30:48.958046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 791
100.0%

Most occurring characters

ValueCountFrequency (%)
0 791
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 791
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 791
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 791
100.0%
Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
Minimum2020-08-04 02:27:56
Maximum2023-03-10 04:21:03
2023-08-04T15:30:49.054129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:49.165474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)

active
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct783
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean846603.1
Minimum-338
Maximum38456997
Zeros8
Zeros (%)1.0%
Negative3
Negative (%)0.4%
Memory size44.6 KiB
2023-08-04T15:30:49.310775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-338
5-th percentile3420.5
Q158984.5
median207505
Q3667300
95-th percentile3393526.5
Maximum38456997
Range38457335
Interquartile range (IQR)608315.5

Descriptive statistics

Standard deviation2382864.6
Coefficient of variation (CV)2.8146184
Kurtosis118.17952
Mean846603.1
Median Absolute Deviation (MAD)188500
Skewness9.3049556
Sum6.6966305 × 108
Variance5.6780438 × 1012
MonotonicityNot monotonic
2023-08-04T15:30:49.492782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
1.0%
13 2
 
0.3%
201555 1
 
0.1%
131543 1
 
0.1%
166246 1
 
0.1%
184006 1
 
0.1%
152476 1
 
0.1%
45981 1
 
0.1%
396005 1
 
0.1%
7412 1
 
0.1%
Other values (773) 773
97.7%
ValueCountFrequency (%)
-338 1
 
0.1%
-2 1
 
0.1%
-1 1
 
0.1%
0 8
1.0%
4 1
 
0.1%
7 1
 
0.1%
13 2
 
0.3%
29 1
 
0.1%
40 1
 
0.1%
49 1
 
0.1%
ValueCountFrequency (%)
38456997 1
0.1%
30581429 1
0.1%
20470039 1
0.1%
16941230 1
0.1%
12028540 1
0.1%
11483808 1
0.1%
9953265 1
0.1%
9914485 1
0.1%
8372830 1
0.1%
8016996 1
0.1%

active_diff
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct232
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.04298
Minimum-13
Maximum25854
Zeros436
Zeros (%)55.1%
Negative4
Negative (%)0.5%
Memory size44.6 KiB
2023-08-04T15:30:49.667020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-13
5-th percentile0
Q10
median0
Q388.5
95-th percentile727
Maximum25854
Range25867
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation1396.168
Coefficient of variation (CV)5.7445314
Kurtosis195.00238
Mean243.04298
Median Absolute Deviation (MAD)0
Skewness12.679607
Sum192247
Variance1949285.2
MonotonicityNot monotonic
2023-08-04T15:30:49.834713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 436
55.1%
1 13
 
1.6%
3 7
 
0.9%
14 7
 
0.9%
4 6
 
0.8%
11 6
 
0.8%
8 5
 
0.6%
2 5
 
0.6%
20 5
 
0.6%
9 5
 
0.6%
Other values (222) 296
37.4%
ValueCountFrequency (%)
-13 1
 
0.1%
-5 1
 
0.1%
-1 2
 
0.3%
0 436
55.1%
1 13
 
1.6%
2 5
 
0.6%
3 7
 
0.9%
4 6
 
0.8%
5 2
 
0.3%
6 4
 
0.5%
ValueCountFrequency (%)
25854 1
0.1%
19197 1
0.1%
10323 1
0.1%
10267 1
0.1%
8285 1
0.1%
7827 1
0.1%
6297 1
0.1%
5262 1
0.1%
3552 1
0.1%
3519 1
0.1%

fatality_rate
Real number (ℝ)

ZEROS 

Distinct323
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.015246397
Minimum0
Maximum0.5336
Zeros36
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:49.994199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0001
Q10.0043
median0.0103
Q30.0191
95-th percentile0.04625
Maximum0.5336
Range0.5336
Interquartile range (IQR)0.0148

Descriptive statistics

Standard deviation0.025190417
Coefficient of variation (CV)1.652221
Kurtosis234.56428
Mean0.015246397
Median Absolute Deviation (MAD)0.0071
Skewness12.432413
Sum12.0599
Variance0.0006345571
MonotonicityNot monotonic
2023-08-04T15:30:50.163898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
 
4.6%
0.002 8
 
1.0%
0.0018 8
 
1.0%
0.0024 8
 
1.0%
0.0001 7
 
0.9%
0.0039 7
 
0.9%
0.0019 7
 
0.9%
0.0101 7
 
0.9%
0.0079 7
 
0.9%
0.0017 7
 
0.9%
Other values (313) 689
87.1%
ValueCountFrequency (%)
0 36
4.6%
0.0001 7
 
0.9%
0.0002 1
 
0.1%
0.0003 3
 
0.4%
0.0004 2
 
0.3%
0.0005 2
 
0.3%
0.0006 4
 
0.5%
0.0007 5
 
0.6%
0.0008 6
 
0.8%
0.0009 2
 
0.3%
ValueCountFrequency (%)
0.5336 1
0.1%
0.2222 1
0.1%
0.1807 1
0.1%
0.0786 1
0.1%
0.078 1
0.1%
0.0757 1
0.1%
0.073 1
0.1%
0.0729 1
0.1%
0.0728 1
0.1%
0.072 1
0.1%

iso
Text

Distinct196
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:50.388324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.0189633
Min length3

Characters and Unicode

Total characters2388
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)21.6%

Sample

1st rowAFG
2nd rowALB
3rd rowDZA
4th rowAND
5th rowAGO
ValueCountFrequency (%)
rus 83
 
10.5%
usa 59
 
7.5%
jpn 49
 
6.2%
ind 37
 
4.7%
col 34
 
4.3%
chn 34
 
4.3%
mex 33
 
4.2%
ukr 28
 
3.5%
bra 27
 
3.4%
per 26
 
3.3%
Other values (186) 381
48.2%
2023-08-04T15:30:50.762886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 240
 
10.1%
R 234
 
9.8%
U 214
 
9.0%
N 196
 
8.2%
A 189
 
7.9%
E 150
 
6.3%
C 121
 
5.1%
P 118
 
4.9%
L 111
 
4.6%
D 93
 
3.9%
Other values (17) 722
30.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2384
99.8%
Dash Punctuation 4
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 240
 
10.1%
R 234
 
9.8%
U 214
 
9.0%
N 196
 
8.2%
A 189
 
7.9%
E 150
 
6.3%
C 121
 
5.1%
P 118
 
4.9%
L 111
 
4.7%
D 93
 
3.9%
Other values (16) 718
30.1%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2384
99.8%
Common 4
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 240
 
10.1%
R 234
 
9.8%
U 214
 
9.0%
N 196
 
8.2%
A 189
 
7.9%
E 150
 
6.3%
C 121
 
5.1%
P 118
 
4.9%
L 111
 
4.7%
D 93
 
3.9%
Other values (16) 718
30.1%
Common
ValueCountFrequency (%)
- 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2388
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 240
 
10.1%
R 234
 
9.8%
U 214
 
9.0%
N 196
 
8.2%
A 189
 
7.9%
E 150
 
6.3%
C 121
 
5.1%
P 118
 
4.9%
L 111
 
4.6%
D 93
 
3.9%
Other values (17) 722
30.2%

name
Text

Distinct196
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:50.906766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length6.5613148
Min length2

Characters and Unicode

Total characters5190
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique171 ?
Unique (%)21.6%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
russia 83
 
9.6%
us 59
 
6.9%
japan 49
 
5.7%
india 37
 
4.3%
colombia 34
 
3.9%
china 34
 
3.9%
mexico 33
 
3.8%
ukraine 28
 
3.3%
brazil 27
 
3.1%
peru 26
 
3.0%
Other values (218) 451
52.4%
2023-08-04T15:30:51.197527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 801
15.4%
i 525
 
10.1%
n 420
 
8.1%
e 342
 
6.6%
s 263
 
5.1%
r 219
 
4.2%
o 206
 
4.0%
l 204
 
3.9%
u 188
 
3.6%
d 177
 
3.4%
Other values (48) 1845
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4196
80.8%
Uppercase Letter 915
 
17.6%
Space Separator 70
 
1.3%
Other Punctuation 3
 
0.1%
Dash Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 801
19.1%
i 525
12.5%
n 420
10.0%
e 342
8.2%
s 263
 
6.3%
r 219
 
5.2%
o 206
 
4.9%
l 204
 
4.9%
u 188
 
4.5%
d 177
 
4.2%
Other values (16) 851
20.3%
Uppercase Letter
ValueCountFrequency (%)
S 128
14.0%
C 115
12.6%
U 110
12.0%
R 88
9.6%
M 67
7.3%
I 67
7.3%
B 59
 
6.4%
J 51
 
5.6%
P 42
 
4.6%
G 31
 
3.4%
Other values (15) 157
17.2%
Other Punctuation
ValueCountFrequency (%)
' 1
33.3%
* 1
33.3%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
70
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5111
98.5%
Common 79
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 801
15.7%
i 525
 
10.3%
n 420
 
8.2%
e 342
 
6.7%
s 263
 
5.1%
r 219
 
4.3%
o 206
 
4.0%
l 204
 
4.0%
u 188
 
3.7%
d 177
 
3.5%
Other values (41) 1766
34.6%
Common
ValueCountFrequency (%)
70
88.6%
- 2
 
2.5%
) 2
 
2.5%
( 2
 
2.5%
' 1
 
1.3%
* 1
 
1.3%
, 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 801
15.4%
i 525
 
10.1%
n 420
 
8.1%
e 342
 
6.6%
s 263
 
5.1%
r 219
 
4.2%
o 206
 
4.0%
l 204
 
3.9%
u 188
 
3.6%
d 177
 
3.4%
Other values (48) 1845
35.5%
Distinct599
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size44.6 KiB
2023-08-04T15:30:51.400721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length31
Mean length8.1441214
Min length0

Characters and Unicode

Total characters6442
Distinct characters60
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique592 ?
Unique (%)74.8%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
oblast 70
 
7.8%
republic 22
 
2.4%
unknown 14
 
1.6%
and 12
 
1.3%
islands 11
 
1.2%
krai 9
 
1.0%
new 7
 
0.8%
pradesh 5
 
0.6%
okrug 5
 
0.6%
autonomous 5
 
0.6%
Other values (662) 742
82.3%
2023-08-04T15:30:51.776213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 920
 
14.3%
n 446
 
6.9%
i 422
 
6.6%
r 357
 
5.5%
o 352
 
5.5%
e 343
 
5.3%
s 335
 
5.2%
l 312
 
4.8%
t 290
 
4.5%
285
 
4.4%
Other values (50) 2380
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5223
81.1%
Uppercase Letter 896
 
13.9%
Space Separator 285
 
4.4%
Dash Punctuation 18
 
0.3%
Other Punctuation 16
 
0.2%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 920
17.6%
n 446
 
8.5%
i 422
 
8.1%
r 357
 
6.8%
o 352
 
6.7%
e 343
 
6.6%
s 335
 
6.4%
l 312
 
6.0%
t 290
 
5.6%
u 222
 
4.3%
Other values (16) 1224
23.4%
Uppercase Letter
ValueCountFrequency (%)
O 94
 
10.5%
S 65
 
7.3%
A 62
 
6.9%
M 61
 
6.8%
C 61
 
6.8%
P 55
 
6.1%
K 52
 
5.8%
N 49
 
5.5%
R 42
 
4.7%
T 39
 
4.4%
Other values (16) 316
35.3%
Other Punctuation
ValueCountFrequency (%)
. 11
68.8%
* 2
 
12.5%
, 2
 
12.5%
' 1
 
6.2%
Space Separator
ValueCountFrequency (%)
285
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6119
95.0%
Common 323
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 920
15.0%
n 446
 
7.3%
i 422
 
6.9%
r 357
 
5.8%
o 352
 
5.8%
e 343
 
5.6%
s 335
 
5.5%
l 312
 
5.1%
t 290
 
4.7%
u 222
 
3.6%
Other values (42) 2120
34.6%
Common
ValueCountFrequency (%)
285
88.2%
- 18
 
5.6%
. 11
 
3.4%
* 2
 
0.6%
, 2
 
0.6%
) 2
 
0.6%
( 2
 
0.6%
' 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 920
 
14.3%
n 446
 
6.9%
i 422
 
6.6%
r 357
 
5.5%
o 352
 
5.5%
e 343
 
5.3%
s 335
 
5.2%
l 312
 
4.8%
t 290
 
4.5%
285
 
4.4%
Other values (50) 2380
36.9%

lat
Real number (ℝ)

MISSING 

Distinct769
Distinct (%)99.2%
Missing16
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean26.682965
Minimum-52.368
Maximum71.7069
Zeros4
Zeros (%)0.5%
Negative125
Negative (%)15.8%
Memory size44.6 KiB
2023-08-04T15:30:51.944499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-52.368
5-th percentile-21.14827
Q18.64495
median33.75943
Q347.176909
95-th percentile58.595202
Maximum71.7069
Range124.0749
Interquartile range (IQR)38.531959

Descriptive statistics

Standard deviation25.591264
Coefficient of variation (CV)0.95908622
Kurtosis-0.22957951
Mean26.682965
Median Absolute Deviation (MAD)17.21447
Skewness-0.7122306
Sum20679.298
Variance654.91279
MonotonicityNot monotonic
2023-08-04T15:30:52.111483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
52.9399 2
 
0.3%
37.6489 2
 
0.3%
13.1939 2
 
0.3%
48.57527615 1
 
0.1%
54.4223954 1
 
0.1%
66.0006475 1
 
0.1%
55.4259922 1
 
0.1%
43.0574916 1
 
0.1%
43.11542075 1
 
0.1%
Other values (759) 759
96.0%
(Missing) 16
 
2.0%
ValueCountFrequency (%)
-52.368 1
0.1%
-51.7963 1
0.1%
-48.4674 1
0.1%
-45.9864 1
0.1%
-41.9198 1
0.1%
-41.4545 1
0.1%
-40.9006 1
0.1%
-40.231 1
0.1%
-38.9489 1
0.1%
-38.4161 1
0.1%
ValueCountFrequency (%)
71.7069 1
0.1%
70.2998 1
0.1%
68.27557185 1
0.1%
68.0000418 1
0.1%
67.1471631 1
0.1%
66.941626 1
0.1%
66.8309 1
0.1%
66.0006475 1
0.1%
65.3337 1
0.1%
64.9631 1
0.1%

long
Real number (ℝ)

MISSING 

Distinct759
Distinct (%)97.9%
Missing16
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean11.409754
Minimum-178.1165
Maximum178.065
Zeros4
Zeros (%)0.5%
Negative299
Negative (%)37.8%
Memory size44.6 KiB
2023-08-04T15:30:52.263117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-178.1165
5-th percentile-102.37908
Q1-68.3816
median14.3754
Q373.10106
95-th percentile138.1542
Maximum178.065
Range356.1815
Interquartile range (IQR)141.48266

Descriptive statistics

Standard deviation78.510938
Coefficient of variation (CV)6.8810367
Kurtosis-0.89007244
Mean11.409754
Median Absolute Deviation (MAD)71.22944
Skewness0.010818123
Sum8842.5596
Variance6163.9674
MonotonicityNot monotonic
2023-08-04T15:30:52.582433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
0.5%
-36.9541 2
 
0.3%
-72.1416 2
 
0.3%
-59.5432 2
 
0.3%
-71.5724 2
 
0.3%
-70.812 2
 
0.3%
-36.782 2
 
0.3%
-74.03 2
 
0.3%
-72.3311 2
 
0.3%
-122.6655 2
 
0.3%
Other values (749) 753
95.2%
(Missing) 16
 
2.0%
ValueCountFrequency (%)
-178.1165 1
0.1%
-175.1982 1
0.1%
-172.1046 1
0.1%
-170.132 1
0.1%
-169.8672 1
0.1%
-168.734 1
0.1%
-159.7777 1
0.1%
-157.4983 1
0.1%
-152.4044 1
0.1%
-149.4068 1
0.1%
ValueCountFrequency (%)
178.065 1
0.1%
177.6493 1
0.1%
174.886 1
0.1%
171.1845 1
0.1%
169.4900869 1
0.1%
166.9592 1
0.1%
166.9315 1
0.1%
165.618 1
0.1%
160.1562 1
0.1%
160.0383819 1
0.1%

Interactions

2023-08-04T15:30:20.478244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:00.619609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.304880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.115014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:23.729319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:31.472495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.207616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:46.833624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:54.659967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:22.719321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:00.747855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.428034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.234629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:23.850689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:31.597630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.327389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:46.953954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:56.725368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:24.787400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:00.871834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.542761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.348636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:23.963676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:31.719662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.444577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.070543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:58.964704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:27.072685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:00.992256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.654655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.453581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:24.066636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:31.834876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.549183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.176227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:01.042262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:29.189192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:01.113808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.759407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.554484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:24.166722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:31.944487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.652686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.279465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:03.283266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:31.460581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:01.240742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.886783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.676565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:24.283792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:32.072495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.772838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.401125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:05.507762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:33.519950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:01.356506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:08.996919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.779994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:24.385456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:32.184420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.879290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.507699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:07.558298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:35.742315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:01.477333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:09.116712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:16.894439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:24.495304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:32.302869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:39.989367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:47.619283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:09.797923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:41.241683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:04.962361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:12.643373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:20.233485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:27.996706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:35.665706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:43.502251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:29:51.153088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-04T15:30:15.029376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-04T15:30:52.697865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
confirmeddeathsconfirmed_diffdeaths_diffactiveactive_difffatality_ratelatlong
confirmed1.0000.8210.3540.3611.0000.3440.0130.0910.112
deaths0.8211.0000.2240.2900.8160.2170.493-0.001-0.001
confirmed_diff0.3540.2241.0000.6660.3530.996-0.0340.2270.203
deaths_diff0.3610.2900.6661.0000.3600.645-0.0510.111-0.002
active1.0000.8160.3530.3601.0000.3430.0070.0930.114
active_diff0.3440.2170.9960.6450.3431.000-0.0330.2220.202
fatality_rate0.0130.493-0.034-0.0510.007-0.0331.000-0.109-0.089
lat0.091-0.0010.2270.1110.0930.222-0.1091.0000.302
long0.112-0.0010.203-0.0020.1140.202-0.0890.3021.000

Missing values

2023-08-04T15:30:46.441201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-04T15:30:46.852029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-04T15:30:46.996331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dateconfirmeddeathsrecoveredconfirmed_diffdeaths_diffrecovered_difflast_updateactiveactive_difffatality_rateisonameprovincelatlong
df_index
02023-03-09209451789600002023-03-10 04:21:0320155500.0377AFGAfghanistan33.939167.7100
12023-03-093344573598014002023-03-10 04:21:03330859140.0108ALBAlbania41.153320.1683
22023-03-09271496688102002023-03-10 04:21:0326461520.0253DZAAlgeria28.03391.6596
32023-03-094789016500002023-03-10 04:21:034772500.0034ANDAndorra42.50631.5218
42023-03-09105288193300002023-03-10 04:21:0310335500.0184AGOAngola-11.202717.8739
52023-03-09910614600002023-03-10 04:21:03896000.0160ATGAntigua and Barbuda17.0608-61.7964
62023-03-091004495713047200002023-03-10 04:21:03991448500.0130ARGArgentina-38.4161-63.6167
72023-03-09447308872700002023-03-10 04:21:0343858100.0195ARMArmenia40.069145.0382
82023-03-092329742280355002023-03-10 04:21:032327463550.0010AUSAustraliaAustralian Capital Territory-35.4735149.0124
92023-03-0939159926529078633602023-03-10 04:21:03390946378270.0017AUSAustraliaNew South Wales-33.8688151.2093
dateconfirmeddeathsrecoveredconfirmed_diffdeaths_diffrecovered_difflast_updateactiveactive_difffatality_rateisonameprovincelatlong
df_index
7812023-03-098981181150005771402023-03-10 04:21:038866185630.0128GBRUnited KingdomWales52.1307-3.7837
7822023-03-091034303761700002023-03-10 04:21:03102668600.0074URYUruguay-32.5228-55.7658
7832023-03-09251247163700002023-03-10 04:21:0324961000.0065UZBUzbekistan41.377564.5853
7842023-03-09120141400002023-03-10 04:21:031200000.0012VUTVanuatu-15.3767166.9592
7852023-03-09552162585405002023-03-10 04:21:0354630850.0106VENVenezuela6.4238-66.5897
7862023-03-09115269944318600002023-03-10 04:21:031148380800.0037VNMVietnam14.0583108.2772
7872023-03-09703228570800002023-03-10 04:21:0369752000.0081PSEWest Bank and Gaza31.952235.2332
7882023-03-0911945215900002023-03-10 04:21:03978600.1807YEMYemen15.55272699999999948.516388
7892023-03-09343135405700002023-03-10 04:21:0333907800.0118ZMBZambia-13.133927.8493
7902023-03-09264276567100002023-03-10 04:21:0325860500.0215ZWEZimbabwe-19.015429.1549